System Maintenance Notice

Please note that maintenance work is scheduled between 4 AM and 5 AM - CET. During this time, you may experience reduced performance. Thanks for your patience.

Publications

Journal Article Research Support, N.I.H., Extramural Ppublish 8 citations

PubMed: 38095106 DOI: 10.1161/ATVBAHA.123.320331

Prediction of Venous Thromboembolism in Diverse Populations Using Machine Learning and Structured Electronic Health Records.

Arterioscler Thromb Vasc Biol 44(2):491-504

Authors: Robert Chen, Ben Omega Petrazzini, Waqas A Malick, Robert S Rosenson, Ron Do

Affiliations

  • Charles Bronfman Institute for Personalized Medicine (R.C. B.O.P. R.D.), Icahn School of Medicine at Mount Sinai, New York.
  • Medical Scientist Training Program (R.C.), Icahn School of Medicine at Mount Sinai, New York.
  • Department of Genetics and Genomic Sciences (R.C. B.O.P. R.D.), Icahn School of Medicine at Mount Sinai, New York.
  • Center for Genomic Data Analytics (B.O.P. R.D.), Icahn School of Medicine at Mount Sinai, New York.
  • The Zena and Michael A. Wiener Cardiovascular Institute (W.A.M. R.S.R.), Icahn School of Medicine at Mount Sinai, New York.

Abstract

BACKGROUND

Venous thromboembolism (VTE) is a major cause of morbidity and mortality worldwide. Current risk assessment tools, such as the Caprini and Padua scores and Wells criteria, have limitations in their applicability and accuracy. This study aimed to develop machine learning models using structured electronic health record data to predict diagnosis and 1-year risk of VTE.

METHODS

We trained and validated models on data from 159 001 participants in the Mount Sinai Data Warehouse. We then externally tested them on 401 723 participants in the UK Biobank and 123 039 participants in All of Us. All data sets contain populations of diverse ancestries and clinical histories. We used these data sets to develop small, medium, and large models with increasing features on a range of optimizing portability to maximizing performance. We make trained models publicly available in click-and-run format at https://doi.org/10.17632/tkwzysr4y6.6.

RESULTS

In the holdout and external test sets, respectively, models achieved areas under the receiver operating characteristic curve of 0.80 to 0.83 and 0.72 to 0.82 for VTE diagnosis prediction and 0.76 to 0.78 and 0.64 to 0.69 for 1-year risk prediction, significantly outperforming the Padua score. Models also demonstrated robust performance across different VTE types and patient subsets, including ethnicity, age, and surgical and hospitalization status. Models identified both established and novel clinical features contributing to VTE risk, offering valuable insights into its underlying pathophysiology.

CONCLUSIONS

Machine learning models using structured electronic health record data can significantly improve VTE diagnosis and 1-year risk prediction in diverse populations. Model probability scores exist on a continuum, affecting mortality risk in both healthy individuals and VTE cases. Integrating these models into electronic health record systems to generate real-time predictions may enhance VTE risk assessment, early detection, and preventative measures, ultimately reducing the morbidity and mortality associated with VTE.

MeSH

Mortality, Risk Assessment, Electronic Health Records, Machine Learning, Arteriosclerosis, Vascular Diseases, Diagnosis, Morbidity, Venous Thromboembolism, Thrombosis, Medical Records

Cited by